BeanFest

BeanFest is a computer game in which participants interact with beans that vary in terms of their shape (from circular to oval to oblong) and number of speckles (from 1 to 10). Different types of beans (e.g., circular beans with few speckles) are associated with either positive or negative outcomes when they are selected. BeanFest was developed by Fazio, Eiser, and Shook (2004) as a paradigm for studying the development of attitudes toward novel objects and the subsequent generalization of those newly-developed attitudes to objects of varying similarity to the original stimuli. The paradigm allows for the assessment of individual differences in the learning of positive versus negative attitudes (what we refer to as the “learning bias”) as well as individual differences in attitude generalization (what we refer to as the valence “weighting bias”). An overview of the program of research is available in a recent Advances in Experimental Social Psychology chapter (Fazio, Pietri, Rocklage, & Shook, 2015).

This webpage is devoted to researchers who may be interested in employing the BeanFest paradigm. Relevant information and resources are noted below.

1. BeanFest Software

We typically run BeanFest via the Inquisit platform. Inquisit is available from http://www.millisecond.com/, and their test library includes some demos and downloadable scripts for running the typical BeanFest procedure, http://www.millisecond.com/download/library/BeanFest/. The script that we ourselves use for running the typical version of BeanFest (points with noncontingent feedback) is available here. This is the implementation we have found most useful for assessing both the learning and the weighting bias.
(See the Resources section below for a useful SPSS syntax file specific to the Inquisit version of BeanFest.)

Matt Rocklage has created a Python version of our typical implementation of the BeanFest software. For those familiar with Python, his BeanFestPy program is available here. [A note of caution: this program does not operate properly under a Windows XP platform.]
If you have any questions about the python version, feel free to email Matt,
matthew.rocklage@kellogg.northwestern.edu. Also, see the Resources section below for a useful SPSS syntax file that Matt developed for this Python version.

Sometime within the next year we hope to complete development of a java-based version of the BeanFest software that we will make available for download.

2. Assessing Individual Differences

The learning bias

Estimating individual differences in the learning of positive versus negative attitudes is very straightforward. We consider participants’ performance during the test phase, i.e., after they played the BeanFest game. During the test phase, participants are presented with each of the 36 beans that were included in the game phase and asked to indicate whether the bean is good (one that increases points) or bad (one that decreases points). We calculate the proportion of positive game beans that a participant classifies correctly and the proportion of negative game beans classified correctly. The difference between these two proportions captures any asymmetry in learning and, hence, serves as the index of the learning bias.

The weighting bias

Estimating individual differences in attitude generalization is more complicated. The test phase includes not only beans that were presented during the course of the game but also novel beans that had not been seen before. These novel beans vary in their resemblance to positive and negative game beans. Such resemblance matters, in that attitudes toward the various game beans do generalize. Novel beans that are more similar to positive game beans are more likely to be classified as positive. Novel beans that bear more resemblance to negative game beans are more likely to be classified as negative. Thus, estimating individual differences in attitude generalization (or what we call valence weighting tendencies) must take into account a given participant’s learning of the positive and negative game beans. For this reason, we assess the weighting bias via a regression equation relating participants’ average response to the novel beans (coded as +1/-1 for positive versus negative responses) to the proportion of positive game beans that they correctly classify and the proportion of negative game beans they correctly classify. Based on an aggregated sample of over 1800 participants, the equation has the following values:

For any given participant, this equation can be used to compute the predicted value of the participant’s average response to the novel beans, in other words, the value expected on the basis of the individual’s pattern of learning. The difference between the actual value (i.e., the average response computed directly from the participant’s data) and the predicted value serves as the estimate of the given participant’s valence weighting bias.

We would recommend that any researchers using the BeanFest paradigm employ the normative regression equation presented above to compute the predicted values and, ultimately, the residuals that index the weighting bias, provided that their participants can be presumed to be similar to our Ohio State University college student sample. If so, the above regression weights, based as they are on an aggregated sample of over 1800, are likely to prove more stable than those calculated on the basis of a sample-specific regression. If there is reason to question the sample’s similarity to our own student sample, then a regression equation specific to the sample may be more appropriate.

This link provides a useful PowerPoint presentation. It is an introductory tutorial describing how to play the BeanFest game. We typically use it to provide initial instructions to the participants.

This link provides a useful SPSS syntax file. After importing the data file created by Inquisit into an SPSS .sav file, the syntax can be used to recode the data. The syntax file ouptuts a new .sav file that includes the BeanFest variables typically of interest. More specifically, the new data file includes variables representing (a) the proportion of positive game beans classified correctly during the test phase, (b) the proportion of negative game beans classified correctly during the test phase, (c) the learning asymmetry, (d) the average response to the novel beans during the test phase, and (e) the valence weighting bias, calculated as described above.